Improving ICP with Easy Implementation for Free Form Surface Matching

Automatic range image registration and matching is an attractive but unresolved problem in both the machine vision and pattern recognition literature. Since automatic range image registration and matching is inherently a very difficult problem, the algorithms developed are becoming more and more complicated. In this paper, we propose a novel practical algorithm for automatic free-form surface matching. This method directly manipulates the possible point matches established by the traditional ICP criterion based on both the collinearity and closeness constraints without any feature extraction, image pre-processing, or motion estimation from outliers corrupted data. A comparative study based on a large number of real range images has shown the accuracy and robustness of the novel algorithm

Segmentation and Determination of Brain Tumor by Bounding Box Method

An Intracranial Neoplasm (Brain Tumor) occurs when abnormal cells form within the brain. There are two main types of tumors: Malignant (Cancerous Tumors) and Benign tumors. Cancerous or non-cancerous mass and growth of abnormal cells in the brain leads to the formation of brain tumor. In order to reduce the increasing fatality rate caused by brain tumor, it is necessary to detect and cure the affected region early and efficiently. Initially, pre-processing is performed, in this phase image is enhanced in the way that finer details are improved and noise is removed from the image. During pre-processing, filters are applied on an input grey scale image to remove unwanted impurities. Filtered image thus obtained is free from impurities. Processing of an image is performed next. Image segmentation is based on the division of the image into regions. Division is done on the basis of similar attributes. Post processing is done using threshold and watershed segmentation. During post processing, the filtered image is forwarded for threshold segmentation along with SVM classifier. Threshold segmentation usually transforms the image in a binary format based on a threshold value. SVM analyze data for classification and regression analysis. Watershed segmentation groups the pixels of image based on their intensities. Morphological operations are applied to the converted image. Boundary extraction is a major part of research which uses fast bounding box algorithm which detects the affected area in motion

Dual registration of abdominal motion for motility assessment in free-breathing data sets acquired using dynamic MRI.

At present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and free-breathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth. DRAM demonstrated an improved ability to propagate ROIs through free-breathing small bowel and colonic motility data, with median error decreased by 90% and 55%, respectively. Comparison between global parametric maps showed high concordance between breath-hold data and free-breathing DRAM. Quantification of segmental and global motility in dynamic MR data is more accurate and robust to respiration when using the DRAM approach

Inclusion agglomeration in electrified molten metal: thermodynamic consideration

The effect of electric current on inclusion agglomeration in molten metal has been investigated. It is found that the agglomeration is dependent on the electric current density, distance between inclusions and orientation of electric field. Electric current retards the agglomeration unless two inclusions are aligned along or closely to the current flow streamlines and the distance between inclusions is less than a critical value. The mechanism is also validated in the computation of cluster agglomeration. The numerical results provide a comprehensive indication for the current-induced inclusion removal and current-induced inclusion elongation. When the inclusions are in long-thin shape, the calculation predicts the current-induced microstructure alignment and current-induced microstructure refinement phenomena

A joint motion & disparity motion estimation technique for 3D integral video compression using evolutionary strategy

3D imaging techniques have the potential to establish a future mass-market in the fields of entertainment and communications. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. Just like any digital video, 3D video sequences must also be compressed in order to make it suitable for consumer domain applications. However, ordinary compression techniques found in state-of-the-art video coding standards such as H.264, MPEG-4 and MPEG-2 are not capable of producing enough compression while preserving the 3D clues. Fortunately, a huge amount of redundancies can be found in an integral video sequence in terms of motion and disparity. This paper discusses a novel approach to use both motion and disparity information to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression. We further propose an optimization technique based on evolutionary strategies to minimize the computational complexity of the joint motion disparity estimation. Experimental results demonstrate that Joint Motion and Disparity Estimation can achieve over 1 dB objective quality gain over normal motion estimation. Once combined with Evolutionary strategy, this can achieve up to 94% computational cost saving

Use of 3D body motion to freeform surface design

This paper presents a novel surface modelling approach by utilising a 3D motion capture system. For designing a large-sized surface, a network of splines is initially set up. Artists or designers wearing motion markers on their hands can then change shapes of the splines with their hands. Literarily they can move their bodies freely to any positions to perform their tasks. They can also move their hands in 3D free space to detail surface characteristics by their gestures. All their design motions are recorded in the motion capturing system and transferred into 3D curves and surfaces correspondingly. This paper reports this novel surface design method and some case studies